April 2018
Beginner to intermediate
566 pages
12h 17m
English
In this section, we will implement the code, which will give us an idea about how good or how bad our trained ML models perform in a validation set. We are using the mean accuracy score and the AUC-ROC score.
Here, we have generated five different classifiers and, after performing testing for each of them on the validation dataset, which is 25% of held-out dataset from the training dataset, we will find out which ML model works well and gives us a reasonable baseline score. So let's look at the code:.

Figure 1.55: Code snippet to obtain a test score for the trained ML model
In the preceding code snippet, you can see the scores ...